Article Per Year (5 Year)
p-Index From 2021 - 2026
8.509
P-Index
This Author published in this journals
All Journal SAMUDERA Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) INFORMAL: Informatics Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JISKa (Jurnal Informatika Sunan Kalijaga) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Informasi dan Teknologi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Multica Science and Technology jeti TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi International Journal of Information System & Innovative Technology Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Scientific Journal of Informatics International Journal of Information System and Innovative Technology Smatika Jurnal : STIKI Informatika Jurnal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rozzi Kesuma Dinata
Program Studi Teknik Informatika, Unversitas Malikussaleh, Aceh Utara 24351, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

Published : 78 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 78 Documents
Search

 2   3   4   5   6 
CLASSIFICATION OF COLOR BLIND STUDENTS AT SMA NEGERI 1 LHOKSEUMAWE USING NAÏVE BAYES ALGORITHM Rozzi Kesuma Dinata; Maryana; Sujacka Retno; Gadis Ayu Sofiana
Multica Science and Technology Vol 3 No 1 (2023): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v3i1.425

Abstract

This research aims to classify students and determine eye conditions with normal and partial color blindness by using Naïve Bayes algorithm. The research dataset was obtained from students at SMA Negeri 1 Lhokseumawe. The variables used in this research were 24 Plate Ishihara Tests with data collection techniques by using interviews, literature studies and questionnaires. Total data in this research are 140 data and divided into 110 training data and 30 testing data. The results showed that from 110 data trained, there were 69 students included in the normal group and 41 students included in the partial group. Then, the 30 testing data were tested into the classification system for color blind students using Naïve Bayes Algorithm. The accuracy level of the test results was 86.67% and 13.33% error.
Meningkatkan Penulisan Karya Tulis Ilmiah dan Publikasi Guru di SMA Negeri 1 Lhokseumawe Ula, Mutammimul; Badriana, Badriana; Dinata, Rozzi Kesuma; Bintoro, Andik; Fuadi, Wahyu
Jurnal Malikussaleh Mengabdi Vol. 2 No. 1 (2023): Jurnal Malikussaleh Mengabdi, April 2023
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v2i1.12357

Abstract

Peningkatan penulisan karya tulis ilmiah guru dapat dilihat dari berapa banyaknya tulisan/karya yang dihasilkan yang sesuai kompetensi yang dimiliki. Guru tidak hanya mengajar siswa di kelas namun juga dituntut untuk dapat menuliskan gagasan atau ide yang dapat dituangkan dalam tulisan yang dipublish di buku maupun jurnal ilmiah baik itu terakreditasi nasional maupun internasional. Salah satu ragam Dewasa ini kompetensi seseorang bisa dilihat dari berapa banyaknya tulisan yang dia buat sesuai kompetensi yang dimiliki. Tujuan pengabdian ini adalah untuk dapat meningkatkan kemampuan menulis dan pengembangan profesi guru yang dipublish di buku maupun jurnal ilmiah baik itu terakreditasi nasional maupun internasional. Metode pelaksanaan pengabdian ini dengan menggunakan library research. Dengan adanya pelaksaan pengabdian ini dapat meningkatkan kemampuan profesionalisme guru sekaligus memperbaiki kualitas pembelajaran dalam mengajar dengan siswa dikelas. selanjutnya publikasi ilmiah bagi guru sebagai prasyarat dalam kenaikan pangkat dan jabatan sehingga memberikan motivasi yang lebih kepada guru untuk dapat membuat karya ilmiah yang lebih berkualitas. Hasil dari pelaksanaan pengabdian ini Dapat menjadi acuan dalam membuat publikasi guru dan dapat menjadi salah satu syarat kenaikan pangkat dan jabatan guru. Eksistensi kompetensi guru bersangkutan dan juga mengembangkan dan menyebarluaskan keilmuan. Kemudian dengan adanya  pelatihan penulisan karya ilmiah bagi guru-guru dapat meningkatkan publikasi imiah bagi guru-guru tersebut. Kegiatan ini dapat membantu peserta guru dalam  menyusun  dan  memperbaiki kerangka  artikel  yang telah dibuat sebelumnya sehingga telah disertakan materi dan wawasan secara teknis maupun teoretis mengenai cara menulis jurnal yang akan diterbitkan jurnal nasional akreditasi.
Classification of Heart Disease Using Modified K-Nearest Neighbor (MKNN) Method Lubis, Aulia Azzahra Ma'aruf; Dinata, Rozzi Kesuma; Aidilof, Hafizh Al Kautsar
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i2.15702

Abstract

Penyakit jantung memiliki banyak jenis dan gejala yang dialami. Penyakit jantung adalah sebuah kondisi ketika organ jantung tidak dapat bekerja sebagaimana fungsinya dengan baik. Jantung adalah organ penting dalam tubuh manusia yang dimana fungsinya adalah memompa darah ke seluruh tubuh. Karena itu dibutuhkannya diagnosa awal untuk pencegahan penyakit jantung dengan memanfaatkan system yang dapat dibuat untuk diagnosa awal pada gejala yang dialami. Yang pada penelitian ini akan menggunakan metode Modified K-Nearest Neighbor (MKNN) dalam mengklasifikasikan penyakit jantung berdasarkan kriteria atau gejala yang ada. Penelitian ini menggunakan 6 kriteria penyakit dan 3 kelas diagnosa penyakit jantung. Dengan melewati beberapa langkah pengerjaan yaitu menghitung jarak Euclidean, menghitung nilai validitas dan terakhir menghitung weight voting dengan mengandalkan nilai K yang telah ditentukan sejak awal perhitungan. Pada penelitian ini telah ditentukan nilai K=5 dan didapat hasil pengujian akurasi sebesar 85%, dengan recall 90% dan precision 85%.
Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method Jasmin, Nadya; Dinata, Rozzi Kesuma; Sahputra, Ilham
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17807

Abstract

This research aims to implement advanced data mining techniques for the classification of vertigo disorders using the Support Vector Machine (SVM) method. Vertigo, characterized by a spinning sensation, can be triggered by various factors such as nervous system disorders and inner ear infections. With the rising prevalence of vertigo patients, there is a pressing need for more effective and efficient diagnostic tools. This study was conducted at Puskesmas Jangka in Bireuen Regency, involving the collection of vertigo patient data from the years 2023-2024. The collected data underwent a comprehensive preprocessing pipeline, including data cleaning, partitioning into training and testing datasets, and subsequent implementation of the SVM algorithm. The performance of the model was evaluated using the Mean Absolute Percentage Error (MAPE), resulting in a MAPE value of 28.47%.
Penerapan Algoritma Random Forest dalam Deteksi dan Klasifikasi Ransomware Alvanof, Mulia; Bustami; Rozzi Kesuma Dinata
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 2 (2024): September 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i2.488

Abstract

Ransomware is a type of malware that blocks access to computer systems or data until a ransom is paid by the victim. Ransomware attacks typically occur due to malicious files that are unknowingly downloaded and installed by the victim onto their computer system. Given the threats and potential losses posed, methods for detecting and classifying ransomware continue to be developed, one of which utilizes the Random Forest machine learning algorithm. Random Forest is chosen for its advantages in handling large datasets, short training time, high prediction accuracy, and its ability to reduce the risk of overfitting. Using 1380 ransomware samples from a dataset with 54 features, 10 best features were selected through Feature Selection where the built Random Forest model successfully predicted ransomware files with an accuracy of 98.79%.
Evaluating The Quality of K-Medoids Clustering on Crime Data in Indonesia Sujacka Retno; Rozzi Kesuma Dinata; Novia Hasdyna
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp274-280

Abstract

This study evaluates the quality of K-Medoids clustering applied to criminal incident data in Indonesia from 2000 to 2023. The analysis compares the clustering performance on both original and normalized datasets using various evaluation metrics, including the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Calinski-Harabasz Index (CH). The findings reveal that the original dataset consistently outperforms the normalized dataset across all metrics. The optimal clustering was achieved in the seventh iteration of the original data, with the lowest DBI (0.438), the highest SS (0.683), NMI (0.916), ARI (0.984), and CHI (57.418). In contrast, the normalized data exhibited higher DBI values and, in some cases, negative Silhouette Scores, indicating less distinct clusters. These results suggest that for this dataset, K-Medoids clustering performs more effectively on the original data without normalization, providing more accurate and well-defined clusters of criminal incidents. This insight is crucial for future research and practical applications in crime data analysis, emphasizing the importance of dataset preprocessing in clustering methodologies.
Enhancing K-Means Clustering Model to Improve Rice Harvest Productivity Areas in Aceh Utara Using Purity Sujacka Retno; Bustami; Rozzi Kesuma Dinata
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.78254

Abstract

To optimize the performance of the clustering process using K-Means, an optimalization approach employing the Purity algorithm is needed. This research was tested on a dataset of rice harvest productivity areas in Aceh Utara Regency by comprehensively analyzing the number of iterations and the DBI values produced by K-Means and Purity K-Means in clustering priority and non-priority rice production areas. This is in line with the efforts of the Regional Government to implement rice production intensification programs in Aceh Utara Regency. From the testing of Purity K-Means, an average of 5, 2, 2, 5, and 3 iterations were obtained from all tested datasets sequentially from 2019 to 2023. Meanwhile, from the testing of conventional K-Means, the average number of iterations obtained was 5.4, 4.8, 4.2, 5.6, and 3.8 iterations, sequentially. This indicates that the clustering performance conducted by Purity K-Means is better than conventional K-Means. The DBI values obtained from Purity K-Means for the entire dataset sequentially are 0.6781, 0.4175, 0.4419, 0.6182, and 0.4973. This value is lower compared to the DBI values obtained from conventional K-Means, which are 0.7178, 0.6025, 0.4971, 0.7222, and 0.5519, respectively. This also indicates that the validity level of the clustering results performed by Purity K-Means is higher than conventional K-Means.
Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions Hasdyna, Novia; Dinata, Rozzi Kesuma; Yafis, Balqis
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.1-15

Abstract

The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to enhance the performance of the K-Means algorithm by integrating the Purity method, with a specific focus on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity K-Means. Integrating the Purity method enhances the efficiency of K-Means by reducing the number of required convergence iterations. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency.
K Fold Cross Validation Analysis for Electricity Meter Classification at PLN Lhoksukon Using K-NN and SVM Methods Zuboili, Zuboili; Dinata, Rozzi Kesuma; Syahputra, Irwanda
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21340

Abstract

Electricity consumption continues to increase every year in line with the increase in national economic growth. Predicting current electricity demand is important to understand the overall electricity supplied to each region. The problem is that currently, the electricity supply in various areas of Lhoksukon has not matched the needs of the community. In addition, problems can arise if the power generated is less than the load power requirements, causing energy shortages in an area. To find out whether the electricity provided is appropriate or not, a classification using Supervised Learning method is used. After classification, we will use K-fold Cross Validation to measure how good the accuracy is between the methods. This study will use 200 electricity meter data consisting of 150 test data and 50 training data with a composition of 75%: 25%. The testing process where the data process that has been divided is then carried out in the testing process where the data process is obtained from manual calculations. So that in this study get results in the form of the K-NN method with 99.3% accuracy, 100% precision, 99.29% recall and the SVM method with 94.00% accuracy, 94.00% precision, 100% recall. And to find out how well the performance of the method is based on Supervised Learning method, it will be checked using K-Fold Cross Validation with the results of K-NN 99.53% and SVM 96.00%, with the conclusion that the K-Nearest Neighbor method has a better accuracy rate.
Sosialisasi dan Pengenalan Dasar Kecerdasan Buatan bagi Santri Dayah Almubarakah Aceh Utara Hasdyna, Novia; Kesuma Dinata, Rozzi; Fajri, T. Irfan; Mutasar, Mutasar; Fadhilah, Cut
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi April - Juni
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i2.5954

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi digital di kalangan santri melalui pengenalan kecerdasan buatan (Artificial Intelligence) di Dayah Almubarakah, Aceh Utara. Permasalahan yang dihadapi adalah masih rendahnya pemahaman santri terhadap teknologi modern, khususnya kecerdasan buatan, yang berpotensi dimanfaatkan dalam pendidikan dan dakwah. Metode pelaksanaan kegiatan meliputi ceramah interaktif, studi kasus, dan demonstrasi aplikasi berbasis kecerdasan buatan yang relevan dengan kehidupan sehari-hari dan nilai-nilai Islam. Materi yang disampaikan mencakup pengenalan dasar Artificial Intelligence, aplikasinya dalam berbagai bidang, serta tantangan dan peluangnya dari perspektif Islam. Hasil kegiatan menunjukkan antusiasme yang tinggi dari peserta, dengan lebih dari 80% santri mampu menjelaskan kembali konsep dasar kecerdasan buatan setelah mengikuti kegiatan. Selain itu, diskusi yang berlangsung menunjukkan adanya minat lanjutan untuk mendalami topik ini dalam konteks dakwah dan pendidikan. Simpulan dari kegiatan ini adalah bahwa pengenalan kecerdasan buatan secara kontekstual dan aplikatif dapat menjadi langkah awal yang efektif dalam membangun literasi digital Islami di kalangan santri, sekaligus membuka wawasan mereka terhadap perkembangan teknologi yang relevan dengan nilai-nilai keislaman.
Co-Authors ., Yustizar Ahmad Fauzi Abdillah Aidilof, Hafizh Al Kautsar Akbar, Hafizal Akram, Rizalul Alvanof, Mulia Andik Bintoro Annisa Afrilia Zahra Annisa Anya Regina Putri Ar Razi Ar Razi Ar Razi, Ar Razi Ardiansyah, Sakha Arif, M. Arif Saputra Arnawan Hasibuan Asrianda Asrianda Azrai Putra Barumun Daulay Badriana, Badriana Baringin Sianipar Berutu, Indah Fachlira Bustami Bustami Bustami Bustami Bustami Chaeroen Niesa Cut Fadhilah Deffiyani Eva Darnila Fadlisyah Fadlisyah Fadlisyah Fajri, T Irfan Fajriana, Fajriana Fiasari, Fiasari Fikria, Putri Fuadi, Wahyu Gadis Ayu Sofiana Hafizal Akbar Haried Novriando Hasan Tahir Hasmar, Muhammad Al Hafiz Irwanda Syahputra Iswari, Syahyana Jasmin, Nadya Khairul Muttaqin Khairunnisa Khairunnisa Khairunnisa Khairunnisa Lubis, Aulia Azzahra Ma'aruf Maryana Maryana Maryana Melita Saldila Muhammad Al Hafiz Hasmar Muhammad Alif Muhammad Arasyi Muhammad Arrayyan Muhammad Fikry Muhammad Iqbal Muhammad Nurfahmi Muhammad Rivai Muhammad Rizal MUHAMMAD RIZAL Munirul Ula Mursyidah Mursyidah Mutammimul Ula Mutasar Muttaqin Muttaqin Narita Taskia Novia Hasdyna Novianda Novianda Nur Azizah Nurwijayanti Rahmat Hidayat Rahmat Hidayat Rahmatin Nisak Risawandi, Risawandi Rizki Suwanda Rizky Fasya Ramdhani Safwandi Safwandi Safwandi Safwandi Sahputra, Ilham Said Fadlan Anshari Selly Alfika Suci Ramadani Sujacka Retno Sujacka Retno Syatriani Jauhari T Irfan Fajri Tahir, Hasan Ulfa, Septia Mulya Yafis, Balqis Yessy Afrillia Yesy Afrillia Zahratul Fitri Zahratul Fitri Zara Yunizar Zuboili, Zuboili Zulfa Zulfa